Application development environment for biological sample assessment processing

ABSTRACT

A system and method for developing applications (Apps) for automated assessment and analysis of processed biological samples. Such samples are obtained, combined with nutrient media and incubated. The incubated samples are imaged and the image information is classified according to predetermined criteria. The classified image information is then evaluated according to Apps derived from classified historical image information in a data base. The classified historical image information is compared with the classified image information to provide guidance on further processing of the biological sample through Apps tailored to process provide sample process guidance tailored to the classifications assigned to the image information.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a national phase entry under 35 U.S.C. § 371of International Application No. PCT/US2018/054368, filed Oct. 4, 2018,which claims the benefit of the filing date of U.S. Provisional PatentApplication No. 62/568,579, filed Oct. 5, 2017, the disclosures of whichare hereby incorporated by reference herein.

BACKGROUND OF THE TECHNOLOGY

There is increased focus on digital imagery of culture plates such asfor detection of microbial growth, colony counting and/oridentification. Systems and techniques for imaging plates for detectingmicrobes are described in PCT Publication No. WO2015/114121, WO2016/172527, and WO 2016/172532, the entireties of which areincorporated by reference herein. Using such techniques (also referredto herein as Kiestra Systems), laboratory staff is no longer required toread plates by direct visual inspection. Shifting laboratory workflowand decision-making to analysis of digital images of culture plates canalso improve efficiency.

Although significant progress has been made regarding imagingtechnologies, it is still sought to extend such imaging technologies tosupport an automated workflow and/or automated diagnostic processes. Inthis regard, it is desirable to develop techniques that may automateinterpretation of culture plate images (e.g., identification of growth,identification of species, susceptibility testing, antibioticsensitivity analysis etc.) and determine the next steps to be performedbased on the automated interpretation. However, development of automatedimage processing logic (e.g., software) for diagnostic indications canbe time consuming given the diversity of specimen types and organismtaxa.

BRIEF SUMMARY OF THE TECHNOLOGY

Disclosed herein is a system for evaluating biological specimens for thepresence of pathogens, the identity of those pathogens and other relatedanalysis and evaluation. The objectives of the systems are speed ofanalysis, accuracy of the analysis and process automation. Such systemstypically obtain digital images of specimens disposed on nutrient mediaand incubated to discern evidence of microbial growth, such growthproviding an indication of the presence of pathogens in the biologicalspecimen. Such systems are described herein as the Kiestra Systems andinclude apparatus such as a camera and lighting to obtain one or moreimages of the incubated specimen, bar code readers for the specimencontainers (e.g. petri dishes that contain inoculated plated media),etc.

Such systems communicate with and are controlled by one or morecustomer-centric Imaging Application (Imaging-related Apps or Apps).Such Imaging Apps may be software that utilize data derived from acollection of historic specimen images to analyze new specimen imagesfor automated identification and/or diagnosis of disease state. The Appscan provide a clinical tool for rapid specimen characterization andreporting of results. The Apps link clinical specimens (from a clinicalsite), to non-patient identifying facts about the specimens (e.g.,non-patient identifying specimen origin information like demographics),process conditions (e.g. incubation time and temperature), processmaterials or environment (e.g. nutrient media) and/or non-patientidentifying test results for the specimen (no-growth of pathogens,growth or otherwise positive identification of the pathogens,enumeration of the identified pathogens). The facts and conditions arereferred to collectively as analytical information herein. Classifyingsuch analytical information in this manner ensures that only the mostrelevant historical processing information is used to develop an app.Therefore, each developed app is created for a narrowly defined purposeand only deployed when the specimen classifiers correspond to the appclassifiers. It is advantageous if the data is not linked to informationthat would identify the patient (i.e. patient deidentification). In oneembodiment, the system automatically deidentifies the specimeninformation and information in response to certain conditions.Deidentification includes providing metadata that links to non-patientidentifying classifying information (e.g. geographic region from whichthe specimen was obtained, type of specimen, etc.) that, in effect,permits the data to be used in systems and methods that cannot retainconfidential patient information. The system may include Apps thatprovide time series processing of images, classified/trained and testeddiagnostic/evaluation algorithms and/or expert systems. An App mayinclude a module wherein a module may be understood to be one or moreprocesses or algorithms for a particular purpose using, for example,image metadata (metadata serves as the classifications described herein)and rules. In some cases, multiple modules may be implemented as apackage. In some embodiments, the data may be stored in a data base thatcan be used to develop an App, train an App, certify an App, test anApp, etc.

The system may provide image analysis processes that may incorporatebest practices, addresses relevant specimen types and automated picking.

To significantly expedite time to market for valuable Imaging Apps, amulti-fold or modular system may be employed. Such a multi-fold systemmay include modules such as:

-   -   a) Define Best-Practice solutions linked to specific media used        to culture the target microorganisms and the taxa of the target        microorganisms that balance utility and development timelines.        For example, test results and images linked to specific media or        taxa are combined and used to develop applications that will        evaluate new specimens classified with the same media and taxa.        The Apps will require enough information to develop a reliable        application but not require so much information that development        of the app is delayed.    -   b) Align an algorithm development cadence (i.e. pace or speed)        that maximally reuses existing algorithms.    -   c) Establish clinical collaboration sites that continuously        generate images and associated meta-data from a diversity of        specimen types, organism taxa, and best-practices media. That        is, specimen information is obtained to build a database of        information that can be used to train, or further train, the        developed applications. This database of fully classified        historical specimen information is referred to as the data lake        herein.    -   d) Generate a database of images with defined criteria that can        be applied to algorithm training and validation/clinical        submission. The database may include information/data        representing images of clinical specimens with linked        manual/standard analysis of truth (quantitation,        identifications, result interpretation) and classifications        (select patient demographics, imaging time and conditions, media        types, etc.). A database infrastructure provides sequestered        data that can be accessed individually as appropriate for        algorithm development, formal verification and validation (V&V),        or clinical submission. This data generation allows for        on-demand prioritization and development of Apps for specific        specimen or media types.    -   e) Adopt a strategy for non-selective media that buckets        classification reflecting colony complexity (i.e., pure        colonies, predominant colonies or complex colonies); and for        identification and sister colonies, the data delimited, for        example, by the type of media (e.g., chromogenic media (i.e.        CHROMagars)) for defined taxa (or those defined taxa with        specific properties on a specific media type (i.e., hemolysis on        BAP) or other information about conditions and reagents used to        obtain the images and test results.    -   f) Limiting certain Apps only to certain imaging        systems/apparatus (e.g., when a 25 mp camera is used, certain        system capabilities are provided, but other imaging apparatus        will provide other system capabilities). Again, delimiting the        Apps to use in certain narrowly prescribed contexts (e.g. sample        type, media type, taxa type, camera type) and only using data to        develop the Apps that corresponds to the prescribed context        provides an App that is more likely useful and accurate for the        specimens that the app is used to evaluate.

The Apps described herein can be further developed and refined asspecimens, media and organisms are validated and obtain regulatoryapproval. For example, a quadrant quantitation App could be implementedinitially for throat swabs and wound, and later for perianal or otherspecimen types as more specimens are processed. This ongoingtraining/development of the Apps provides a robust cadence of newImaging Apps with increasing usefulness. For example, as more images areevaluated, the App “learns” how to differentiate colonies frombackground in the image of the plated culture. Processing imageinformation to differentiate pixels associated with the image of thecolony from the pixels associated with the image of the background aredescribed in the Kiestra Systems referenced previously herein.

Also described herein is a development system that provides high-valuesoftware solutions while reducing time to market and maximizingutilization of resources. An important component of the system mayinvolve initiating a collaboration program that engages select clinicalsites running imaging systems (e.g., Kiestra Systems) to gather clinicalspecimen imaging information that is classified by association withmeta-data for development (algorithm training) and validation(submission-ready) purposes. This collection of data may feed automatedalgorithm refinement, improving performance and decreasing developmenttime. Validation uses a sequestered collection of images in thedatabase. These data can also be reused as additional features areimplemented. However, the information is only deployed when the dataclassification corresponds to the App classification/purpose. Thisapproach creates substantial efficiency, flexibility in App scope (i.e.,versioning) and a highly-valuable resource.

For non-selective media, classification buckets are created thatcharacterize the complexity of the population to identify cultures as nogrowth, pure, predominate or complex/mixed rather than attempting toidentify all sister colonies of all taxa on all media types. Sistercolony identification is technically very challenging and timeconsuming, particularly on non-CHROMagar media. Mixed cultures typicallyrequire expert knowledge to interpret, and even with a level ofautomated image analysis, would likely require review for confirmationand release. By using the pure, predominate and complex/mixed categoriesmost specimens can be characterized and those with pure or predominatecolony populations can be automatically worked up with a high degree ofconfidence. These colonies can then be chosen by the App for automatedpicking by a picking system. This approach largely renders the need toindividually characterize and define specifications of each specimentype (e.g., sputum, wound, swab, etc.) by/verses a particular pathogen,to a more generic classification strategy that efficiently leverages theimaging algorithms and enables most specimen types to be characterized.

Example Imaging Apps are summarized below. Such applications may include

-   -   MRSA screening Imaging App;    -   Urine 2.0 Imaging App;    -   Rapid detection Imaging App; and    -   Quadrant quantitation Imaging App.

For example, in response to a MRSA (Methicillin-resistant Staphylococcusaureus) screening analysis, the following actions may include: (i.e. fora negative MRSA screening result): i) empiric treatment with a certainset of antibiotics not used with resistant Staph aureus; ii) notsequestering or specifically managing the patient; iii) proceeding witha certain next medical step such as surgery; iv) the ordering of certainadditional diagnostic tests; and v) guidance in selection of a certainset of likely effective antibiotics for determination of antibioticsusceptibility testing. An application developed according the methoddescribed herein can guide a user to some or all of the above actionsbased on historical analysis of prior specimens that share certainpredetermined criteria with the specimen being evaluated by the App.

Actions from a Rapid Detection Imaging App may include: i) communicatingto the treating physician the detection of threshold levels of growthindicating infection many hours sooner than standard methods ofpractice; ii) initiating diagnostics that rapidly determine theidentification of growing pathogens (e.g., via MALDI-tof); iii)communicating the pathogen identification to the physician substantiallyearlier than standard practice; iv) initiating diagnostics fordetermination of antimicrobial susceptibility and communicatingantibiotic susceptibility profiles substantially sooner than currentpractices. These objectives are accomplished by using prior images andthe results of that image analysis to inform analysis of the presentimage. The prior images must be classified to develop an App that issufficiently skilled and reliable to be used to evaluate the presentimage. Note that, if the image analysis performed by the App results ina positive detection of microbial growth, in one embodiment the App cancommunicate with an analyzer that will receive the specimen underevaluation and identify the colony or colonies on the specimen to bepicked for further analysis. The App can either instruct downstreamprocess or control downstream processing Such downstream processingincludes: i) preparing a suspension of the one or more picked colonyinto a pre-determined buffer or solution; ii) adjustment of saidsuspended cell suspension to a predetermined cell concentration; iii)the spotting of said cell suspension to a substrate (e.g., MALDI Plate);iv) overlaying of said spots with one or more reagents including, forexample, MALDI matrix solution, extraction chemicals; v) distributingone or more aliquots of said cell suspension into wells of anantimicrobial susceptibility plate for determination of susceptibilityprofiles to a series of antibiotics at various concentration; and vi)distribution into suspensions for analysis by PCR, sequencing or othermolecular diagnostic tests. In alternative embodiments the App instructsbut does not control follow-on sample processing/analysis.

In order for the Apps developed according to the methods describedherein to be deployed to control processes and evaluate specimens, theApps must be developed using relevant data and analysis. Methods andequipment that can be used to obtain relevant data and analysis include,clinical sites collecting images and reference data, that are used tobuild data sets for future Imaging Apps training and validations. SeeFIG. 1 . This typically involves resources to identify, initiate, andmanage the collaborations and data. Software tools gather data andmeta-data from the clinical sites. Technical resources may need to beprovided in the lab to generate and assign the classifications asmetadata, such as from Image Processing, not normally acquired intypical clinical workup.

The digital cameras (which are conventional cameras with good megapixelresolution, e.g., 5 MP, 25 MP, etc.) may be implemented for adequateperformance for early growth/no growth and presumptive identifications(IDs). For example, for a Colony greater than or equal to 5 mm diameterdetected by the imaging module or apparatus that captures the digitalimage of the inoculated plate, this data could be combined with theGrowth/No Growth Detection to indicate when growth occurs withsufficient colony size for further processing. The imaging apparatus mayitself be an App that performs analysis of the digital image, such asdescribed in the Kiestra Systems references elsewhere herein. Accordingto those systems and methods, colonies are differentiated frombackground and from that image analysis the density of the colonies onthe inoculate plate is determined and communicated to an App that willdetermine further actions in response to the image analysis. Thisinformation could then be used by the App to determine what colony topick from the image automatically (without an operator reading the plateor identifying the colony for pick). The Apps are developed and deployedfor specific plate environments. For example, pure plates (one colonytype) and predominant plates (more than one colony type butpredominantly one colony type) can have representatives of each colonytype indicated by the purity plate module with associated rules thatdetermine further workup. Plates determined to be complex are processedby a different App (or an App that fires different rules). Apre-determined number of each colony type could be designated forautomatic identification (ID) and Antibiotic Susceptibility Testing(AST) workup on an ID/AST module that may involve an automatic pickingsystem/robot.

To decrease product development cost and reduce time to market BestPractices support may be adopted. Specific media (below) may be used toobtain optimal recovery and performance on the platform (e.g., BDKiestra systems).

The system may involve processing of plated media readings performed ina lab. Plated media are prepared as described elsewhere herein. Themedia type and sample taxa are examples of specimen classifiers thatinform the metadata linked to the specimen information. The selected Appmay provide presumptive ID with picking recommendations of only the pureor predominant colony types on the plate. Specimens with multipleclinically significant isolates are infrequent and often complex.Specimens identified as complex may be put into a mixed category formanual review and actioning.

A set of algorithms may be designed that are more generic rather thanspecific to an App configured for use with a specific type of specimenor the suspected target species contained in the specimen. Thesealgorithm tools are not described in detail herein but can be developedand deployed by those skilled in the art. The algorithms are validatedas a process for deployment in Apps designed to evaluate and process arange of specimen types. When such tools are not limited to specificspecimen types, they can be used to review a larger number of plates oras building blocks of other Apps.

The system develops a set of tools that provide an overall capabilitythat can be supplemented in the future as more specimen processing data(e.g. imaging data) is obtained and added to the database with theassociated classifications. These tools will evaluate plates and providea specific result independent of specimen type. A compilation of theseresults along with specimen and patient demographics will be processedby the App to provide an indication of the specimen quantity. Thissimplifies execution and leads to a faster time to market whileproviding the user with a level of flexibility. Development of a moregeneric toolbox of image analysis algorithms (together with theclassification metadata, and rules termed a Module) that enable the Appcan be rapidly matured or optimized (for a particular specimen, forexample) by deploying artificial intelligence algorithms such as neuralnetworks, artificial neural networks or deep learning algorithms thatuse the classified specimen data in the database to make determinationsabout the subject specimen. As part of the exponential strategy,artificial intelligence may be employed that “automatically” determineswhat attributes of the image, and with what algorithms, to best providethe desired Module capability. Individual Modules may have sufficientvalue to be launched as an App, or multiple Modules may be packaged intoan App. In some versions, a set of modules may be differentiated to makethe following specimen analysis based on the specimen informationobtained and its classification: (a) Growth/No Growth; (b)Semi-quantification; (c) Presumptive ID (d) Pure, Predominant, Complex(e) Antibiotic sensitivity (Kirby-Bauer test) (f) Sister colony locator.

Example applications (Apps) may include Key ID, Rapid Detection,CHROMagar ID 2.0, Quadrant Quantitation and Presumptive ID. These may besummarized as follows:

Key ID is a tool that identifies specific species on specific plateswhere any numbers of colonies are present. Any number of coloniestypical of the Key ID app will be designated in pure or mixed cultures.Examples are:

-   -   1. MRSA screening on MRSA II;    -   2. Group A Beta Strep on BAP (Blood Agar Plate); and    -   3. Strep pneumoniae on BAP.

The Rapid Detection App provides rapid detection processing. Growth atany reading point can be used as a flag for growth detected as early aspossible on any plate. The user selects the reading point that willserve as a flag, recognizing the trade-off between an earlier readingpoint that may be less reliable but delivers results more quickly and alater reading point which may be more reliable but take more time todetection. The action taken will be dependent on the reading pointschosen and the rules invoked. Early detection may happen as early as 4hours, but incubation periods of 6 hours, 8 hours or longer arecontemplated. Incubation times for a particular specimen are readilyascertained by one skilled in the art. The system and method describedherein is not limited to any particular incubation time. The RapidDetection App is beneficial for rapid positives of critical specimens.

For example, growth is detected at 8 hours on a BAP plate. A rule wouldtake these results and if the specimen type is CSF (Cerebrospinal fluid)the lab would be alerted immediately if the App determined that thespecimen had growth on the plates because CSF is typically sterile. Thisdetermination requires the App to make some sort of alert in response toa determination that the CSF specimen is positive for pathogens. Forother specimen types, i.e., sputum, early detection would have littlevalue since almost all culture have normal flora. In these cases, norule would be written for specimens so classified and no action would betaken by the App based on rapid detection. Thus, the type of plate andtype of detection can trigger different automated processes.

Another embodiment is a CHROMagar ID App that quantitates urines orother specimen types. This App obtains classified image information thatwill enable the App to presumptively identify the pure or predominatecolony type on CHROMagar Orientation. Mixed plates will also beidentified but the App may fire different rules in response to thedetermination of a complex plate. Using the rules engine, the processedimages of specimens can be sorted into several categories. Certaincategories permit auto reporting of results. Those categories aredescribed elsewhere in detail herein.

The Quadrant Quantitation and Presumptive ID app is a tool that willevaluate all positives to classify each as one of (1) no growth, (2)pure, (3) predominate or (4) complex and the overall quantity on theplate. The pure or predominate isolates will be presumptively identifiedand colonies identified for picking. In this case all cultures would beanalyzed and would fall into several categories with some being autoreported and others sent for review. The plates with significantpathogens could be sent to other systems (e.g., picking or testing) forpicking with no customer/clinician intervention. The Table in FIG. 3 isa listing of the target organism that will be presumptively identifiedon the referenced plates. The Table in FIG. 4 is a listing of platescommonly inoculated for specific specimens (Best Practices).

The environment (e.g., FIG. 1 ) may provide a Software DevelopmentWorkflow. This development effort will focus on completing the workflowand implementing the detection of the organism groups in FIG. 3 on thetarget media in FIG. 4 . The flowchart in FIG. 2 outlines the generalstrategy for algorithm and software development for one or more Apps insupport of an analytical workflow for digital image evaluation. Allimages of the inoculated and incubated culture plates are evaluated bythe modules as outlined in FIG. 2 and described below. Each moduleevaluates a specific result or discrete group of results and is largelyindependent of specimen type. However, metadata associated with thespecimen (which is used to classify the specimen) can also guide furtherprocessing (e.g., incubation instructions, imaging instructions, etc.).Such metadata can be read from a barcode on the specimen container (i.e.the plate or Petri dish). When results are available, processing by anexpert system evaluates these results and recommends or takes theappropriate action. For example, when the App indicates that a specimenshould be evaluated by AST, the expert system will provide guidancerules for the AST panel That guidance typically takes into considerationregulatory or guidance positions (i.e. by FDA or CLSI) as well aslimitations on the proven abilities of the AST test platforms (i.e.limitations). An expert system is provided with a base set of rules. TheApp itself may also fire rules based on information that the App learnsabout the subject image of the subject specimen, even though the App isnot, in and of itself, an expert system. These rules could be edited oradditional rules developed by the user specific to their institution.

Once the plates are evaluated and results are combined at the specimenlevel another set of rules would drive auto reporting, user, LaboratoryInformation System (LIS) or Laboratory Information Management System(LIMS) alerts, and send significant isolates to a worklist or to apicking system such as for ID/AST testing.

By targeting organisms on media independent of the specimen typecomplete systems may be developed more efficiently and rapidly. Forexample, E. coli may be considered the same genus and species no matterthe specimen source for the E. Coli. Regulatory approval of some Apps byspecimen type could be difficult since positives for some specimens(i.e. CSF and other sterile sites) are rare and therefor there would notbe sufficient historical sample processing/image data from which todevelop an App that would be clinically reliable. However, over a periodof time and with the benefit of images and test results from thoseimages obtained from a variety of sources (i.e. clinical trials,regulatory submissions), Apps for even rare specimens can be developed.

One embodiment is a method for processing a biological sample includingthe steps of obtaining a biological sample, combining the biologicalsample with nutrient media, incubating the biological sample, obtaininga digital image of the incubated biological sample, classifying thedigital image according to analytical criteria selected from the groupincluding of specimen origin information, clinical sample criteria,process materials and process conditions, obtaining data from historicaldigital images of incubated biological samples on nutrient media thatshare at least one of the analytical criteria assigned to the digitalimage and using the historical digital image data to output aninstruction to a user for further processing of the biological sample.

The specimen origin information includes geographic informationregarding the biological sample source and the type of biologicalsample. The process materials include the type of nutrient media. Thehistorical digital image data is classified by at least one of specimentype, organism taxa or culture media type. The method further includesanalyzing the digital image data and determining, from the analyzed dataif the digital image reflects microbial growth. In response to adetermination that the digital image does not show microbial growth, themethod outputs an indication of no microbial growth. In response todetermining that there is an indication of microbial growth, the methodperforms the step of determining if the specimen is a sterile specimenand identifying one or more coordinates of a colony of microorganisms inthe image that was a basis for the indication of microbial growth. Inresponse to determining that the specimen is sterile, the methodincludes the further step of indicating that the specimen is a highvalue positive and sending instructions to further process the specimen.Examples of further processing includes identification (ID) testing,antibiotic susceptibility testing or both. The coordinates of an objectin the image classified as a colony are communicated to a module thatforwards those coordinates to a picking apparatus that will pick thecolony from the biological sample. A module may be an App or acombination of Apps as described herein. The biological sample istransferred to the picking apparatus and the colony is picked from thebiological sample wherein the steps of transferring and picking areeither controlled by the module or the module has issued an instructionto perform such steps. In response to determining that the specimen isnot sterile, the historical image data is compared with the image datato identify a specific predetermined species of microorganism in thedigital image of the incubated biological sample. The step of comparingis performed by the module, and, if the module determines that thespecific predetermined species of microorganism is present in the imagedata, the module reports the identification of the specificpredetermined species. When such a determination is made the moduleflags the specimen for further review.

The module compares the historical image data with the digital image ofthe incubated biological sample and determines an amount of microbialgrowth where the comparing and determining steps are performed in themodule in communication with the imaging apparatus that obtained thedigital image of the incubated biological sample. According to themethod, a module or App determines if the microbial growth is one ofpure colonies, predominant colonies, or complex colonies bycommunicating the digital image of the incubated biological sample to amodule that determines a growth level as a vector of threeprobabilities. In response to a determination that the colony is pure,the module reports that the plate is pure. If the growth on the pureplate exceeds a predetermined threshold growth the biological sample isidentified as a high value positive and the module communicates thatinformation to a user of the system. The module, having received thecoordinates of the colony from the imaging apparatus, will communicatethe coordinates to a picking apparatus that will pick the colony fromthe biological sample. The method may also include transferring thebiological sample to the picking apparatus and picking the colony fromthe biological sample where the steps of transferring and picking arecontrolled by the module or requested or required by the module. If themodule determines that the growth does not exceed the predeterminedthreshold the module provides a presumptive identification of the colonybased on comparing an image of the colony provided to the module withthe historical image data accessed by them module. In response to thedetermination the module performs the further step of reporting thepresumptive ID to a user.

If the module determines that the growth does not exceed thepredetermined threshold the module performs the further step ofreporting to a user that the complex sample does not meet or exceed thepositive growth threshold. In response to a determination that thecolony is predominant, the module provides a presumptive identificationof the colony based on comparing an image of the colony provided to themodule with the historical image data accessed by the module. The moduleperforms the further step of reporting the presumptive ID to a user.

If the module determines that the growth exceeds a predeterminedthreshold growth, the module identifies the sample as a high valuepositive. The method also includes alerting a user of the high valuepositive. The method may also include identifying the coordinates of thehigh value positive. The method may also include communicating thecoordinates of a colony to a module that communicates the coordinates toa picking apparatus that will pick the colony from the biologicalsample. The method may also include the module controlling or issuinginstructions to transfer the biological sample to the picking apparatusand picking the colony from the biological sample where the steps oftransferring and picking are controlled by the module. The method mayalso include alerting a user that further review is required. If themodule determines that the growth does not exceed the predeterminedthreshold the module performs the further step of reporting thepresumptive ID to a user. In response to a determination that the colonyis complex, the method further includes: reporting, from the module,that the plate is complex. The method may also include determining, bythe module, if the growth exceeds a predetermined threshold growth. Ifthe module determines that the growth exceeds the predeterminedthreshold growth, the method further includes: alerting a user thatfurther review is required.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example system environment forbiological image processing application development.

FIG. 2 is an example flow diagram of an implementation with example Appsand their processes in a development environment according to an aspectof the disclosure.

FIG. 3 is a table illustrating an example of target organisms withpresumptive identifications and the associated media.

FIG. 4 is table illustrating example targeted media with the associatedspecimen types.

FIG. 5 is a schematic of processes that may be implemented in someversions of the present technology, such as in a processing system 101of in FIG. 1 , where the processing system may access data and/oralgorithms of a Data Lake as discussed herein to develop Apps with atraining process, validation process and/or clinical submission process.These processes may in turn employ information (e.g., updated data andalgorithms) also derived from the Data Lake.

DETAILED DESCRIPTION

The present disclosure provides apparatus and methods of an environmentfor developing imaging applications for identifying and analyzingbiological specimens such as microbial growth. Many of the methodsdescribed herein can be fully or partially automated, such as beingintegrated as part of a fully or partially automated laboratoryworkflow.

This document provides a description of the design and implementation ofa system that accelerates delivery of automated imaging capabilities tobiological imaging systems such as the BD Kiestra™ System. The imagingcapabilities of such systems are enabled by a suite of hardware,software, analytical algorithms, and clinical rules. An example of onesuch commercialized system includes one or more digital cameras (e.g., 4MP) with multiple illumination configurations that generate an optimizedand standardized image based on an appropriate platform. The systemsdescribed herein are capable of being implemented in other opticalsystems for imaging microbiology samples. There are many suchcommercially available systems, which are not described in detailherein. One example may be the BD Kiestra™ ReadA Compact intelligentincubation and imaging system. The Kiestra™ ReadA compact is anautomated incubator with an integrated camera and plate transport systemthat enables automated imaging of plates. The ReadA compact iscommercially available. The ReadA compact also has integrated plateimport and plate export devices that couple the incubator to otherinstruments for manipulation. Therefore, in some embodiments, inresponse to the analysis of the digital images by one or more of theApps, the Apps can issue instructions and control the incubation of therelevant specimen under evaluation by the App. Other example systemsinclude those described in PCT Publication No. WO2015/114121 and U.S.Patent Publication 2015/0299639, the entirety of which is incorporatedby reference herein. Such optical imaging platforms are well known tothose skilled in the art and not described in detail herein.

A series of Apps for the system can provide analysis of the images frommost specimens, generated at various predetermined times, such as in theReadA Compact. The system can enable downstream actioning of theseplates-including automated release of no-growth and/or negative plates,and automated characterization of colonies for definitive ID and ASTanalysis.

At a high level, the imaging analysis tools (Imaging Apps) can bedeployed to enable a collection of different results that providesubstantial value to the lab and/or actionable clinical results. TheseApps utilize one or more image analysis algorithms (Modules), as well asa set of rules that provide information on how to apply the Modules onspecific media types and/or specific specimens. Certain Apps can alsohave associated Expert Systems, which overlay an additional set of ruleson more basic App determinations, typically regulatory/clinicalguidance, that inform recommendations for actioning and interpreting theresult.

Developing Imaging Apps can utilize an iterative approach. A data lakeis developed using either specimen information acquisition or images ofclinical specimens being processed as normal practice in the clinicallab, or both. Algorithms are developed to modelconclusions/instructions/outputs such as those illustrated in FIG. 2that can be drawn from the truths and classification informationassociated with a specimen image evaluated by the App. Validation andverification (V&V) of the App is performed by using a collection ofpredefined images with certain metadata requirements from the data lakethat are marked for V&V analysis. The Apps are then subjected to aclinical study (which includes processing specimens to obtain images ofthose samples and evaluating the images of the specimens using the App)to both verify the App and train the App. The clinical studies do notrequire that a specific clinical site is used for the clinical study

However, a biological image processing application development system100, such as one illustrated in FIG. 1 , may be implemented to leveragean exponential strategy with several components. The system 100 includesone or more of:

-   -   a) a set of toolbox applications, such as for processing system        101 with one or more processors, having generic algorithms and        Modules that enable the App and can be rapidly matured or        optimized (for a type of specimen, for example) by applying        Artificial Intelligence algorithms such as neural networks,        artificial neural networks, and other deep learning algorithms.        Artificial intelligence may be implemented to “automatically”        determine what attributes of an image, and with what algorithms,        to best provide the desired Module capability.    -   b) critical to the development and deployment of the Apps        described herein is a developed database 102, denominated a Data        Lake herein, that comprises images of clinical specimens with        linked manual/standard analysis of “truth” (i.e. facts regarding        the mage such as colony quantitation, IDs, result        interpretation, etc.) and image conditions, and in some cases        patient demographic data (suitably disidentified). The images        carry classification information in the form of metadata so that        only relevant image data is used to develop a particular App.        The Data Lake may be populated by clinical lab collaboration        such as with imaging systems 104 that may optionally supply data        to the database via a network.

Where the Data Lake is stored is largely a matter of design choice. TheData Lake can be stored locally or in a cloud. The data in the Data Lakecan be partitioned. For example, the data in the Data Lake could besegmented depending on how the data is accessed and/or used. In oneembodiment, one segment of the data in the Data Lake could be foralgorithm training, another segment could be used for data verificationor validation and yet another could be used for clinical submission.

The database of the system can store and provide classified data ofthese clinical images and also linked data, that can be pulledindividually as appropriate for algorithm development, formalverification and validation, or clinical submission on demand. The Appcan be established with a level of global standardization including labprotocols, media types, imaging time, quantitation scoring, etc.Resources can supplement the generation of images and reference data(specimens and/or spiked/contrived samples) forspecimens/organisms/conditions that are infrequently encountered in theclinical setting. This proactive strategy for data generation allows analmost on-demand prioritization and development of particular Apps forspecific specimen or media types. The system architecture permitsintegration of the new modules into existing laboratory site software toease release of new imaging diagnostic functionalities(Apps/modules/packages). This may be achieved by standardizing theinterface between new Apps/modules/packages with existing/previousimaging software systems. In this regard, the Apps/modules/packages maybe add-ons to system software such as a system software for an automatedimaging system (e.g., in a processing system that controls any one ormore of a plate/sample conveyor, incubator, camera, picking machineand/or related robotics, for moving such samples/plates within suchautomated laboratory cell/equipment etc.). Certain specimens determinedto be negative for pathogens can be spiked with known pathogens and thesubsequent image of the incubated, spiked specimen characterized asdescribed herein. The images of the spiked specimens can then be used asa training set for Apps that can be used to evaluate and process lessfrequently occurring pathogens.

This approach can provide maximal flexibility in prioritization and thecadence of App development. It would also make it possible to provideearly App performance metrics to help better understand the added valueof an App, the synergies at the solution level, and enable earlierrecognition. The Data Lake may be generated such as with one or morehospital systems that meet a list of Corporate Clinical Development(CCD) criteria (i.e. technical and medical information). Implementationprovides the ability to store and classify the data, query, and auditthe database; lab protocols that define the program and processes;training, monitoring, compliance, quality metrics etc. as is typical fora clinical trial- and may be done as part of the development of the DataLake, rather than at the end of a typical product development process.The approach may implement dedicated clinical lab resources to determinecertain plate results outside standard protocols, and to link imageswith analysis results to the Data Lake. Certain specimens, plate typesor image acquisition time points may need to be run specifically todevelop the Data Lake in special processes that may be independent ofnormal/typical lab practice. Thus, in some versions, the Data Lakedatabase may comprise images of clinical specimens with linkedmanual/standard analysis of truth (e.g., quantitation, IDs, resultinterpretation) and metadata associated with classifications for theimage (e.g., select patient demographics, imaging time and conditions,media types, etc.).

Establishment of both the algorithms and Data Lake may include a certainlevel of standardization. This will also define what any given App isvalidated for, and the analysis/instruction/output that may ultimatelybe obtained. Given the diversity of media types, vendors of media, andincubation times in use across labs globally, a “Best Practices”approach may be used to initiate this effort. Additional conditions maybe added in the future by stocking the Data Lake with appropriate data.An example of a Media x Specimen matrix is summarized in FIG. 3 . Thetable in FIG. 3 includes 12 media types. Note that Blood Agar, trypticsoy broth (TSA) and Columbia are grouped together as one media type. XLDis Xylose Lysine Deoxycholate Agar, SS agar is Salmonella, Shigellaagar, CNA is Columbia Naladixic Acid Agar (CNA) and CLED isCystine-Lactose-Electrolyte-Deficient agar. The listed media are wellknown to those skilled in the art as are the microorganisms known to beidentifiable on the listed media. Thus, the standardization may includelab protocols, media types, imaging time, streak patterns, quantitationscoring, etc. that are classifications applied to the data for thedatabase development and image analysis with reference to the database/Data Lake/historical image information. This focuses validationefforts and minimizes time for development and allows lab to labmetrics, data sharing, etc. The use of the Data Lake as training data,validation data, clinical submission data, etc. for developing Apps isillustrated in FIG. 5 .

To ensure database accuracy, population of the data into the databasemay involve independent human image analysis of images performed byseveral individuals, or plate analysis done manually by humantechnologists. Human readings may be compared with clinical laboratoryreports. In some cases, a further image review may be involved fordiscrepant readings. Database input may involve de-identification ofpatient information from image related data. Review of images for dataentry may involve human scoring of the growth on plates for pure,predominant, complex and no growth; quadrant quantitation.

Example Software Modules for Toolbox

In a typical example, the Data Lake may contain media plate images,linked to lab-determined quantitation (e.g., no growth, +, ++, +++). Itmay also contain identification (ID) of organisms determined to be ofsignificance for the kind or type of specimen (such as ones that may besignificant a trained clinical microbiologist). The Data Lake may alsocontain image-based metadata, and in some cases, patient demographicinformation. Organisms not routinely identified as pathogens (i.e.,normal flora) may also be requested to be identified to facilitatealgorithm development. Once populated, a portion of images will beleveraged to train and test appropriate algorithms toward Appdevelopment.

At a high level, the imaging analysis tools can be deployed to enable acollection of different results that provide substantial value to thelab and/or actionable clinical results. These Modules (Mods) arecomprised of one or more image analysis algorithms, as well as a set ofrules that provide information on how to apply the Modules on specificmedia types and specific specimens. Some Modules, such asScreening-MRSA, may be implemented as an App. Other modules may moreoften be packaged with other Modules to provide a synergistic capability(for example, quadrant quantitation and purity will often be packaged asan App.) Some Apps can also have associated Expert Systems, whichoverlay an additional set of rules, typically regulatory/clinicalguidance, that inform recommendations for actioning and interpreting theresult (e.g. KB Zone described herein). Based on technical and clinicalconsiderations, one or more Apps may also be bundled as components of aLaunch Package depending on different clinical lab needs. It is alsoanticipated that some Apps will have versions (e.g., UCA V1.8 with FDAapproval will become UCA 2.0).

Some examples of the algorithms and Modules (collections of synergisticalgorithms) are generic (generally working across specimens, pathogens,media) and are summarized below and may be considered in relation to theprocesses of the illustration of FIG. 2 . As previously mentioned, theclassification of functionality of the various applications helps toprovide development of detection applications for various species andmedia.

1. Growth App/Module 1010A

Growth App 1010 (See FIG. 2 ) may be directed to answering a simplequestion: is there anything growing that can be detected on this plateat this particular incubation time? The answer to this question will bea growth probability ranging from 0 to 1. Growth can be a moduletargeting any media, independent from dispense volume or streakingpattern. Growth may be detected as early as possible from pre-setimaging points. Rules specify whether to issue an alert based onspecimen type and/or media. In some versions, gram stain results couldalso be integrated with App/Module where appropriate. In some cases,this may be implemented an Early Detection or Early Growth App/Module.Growth may be detected as early as possible (e.g., a detection window of4 to 14 hours or more) from pre-set imaging points. Rules specifywhether to issue an alert based on specimen type and/or media. In someversions, gram stain results could also be integrated here whereappropriate.

2. Key ID App/Module 1020

A Key Id Module 1020 (See, FIG. 2 ) may be aimed at identifying aspecies potentially growing on a given media. For each and everyrequested Key ID organism, the module may provide a list (withprobabilities) of colony locations per Key ID ordered by decreasingprobability. These colonies can then be picked manually or by anautomated picking system.

In some versions, the system may include a Screening and CriticalPathogens module(s) developed on a pathogen basis. These modules mayprovide detection of specific pathogens, groups of pathogens or thosewith specific properties. The Screening Apps may be implemented toenable identification of specific pathogens on CHROMagar, and can beused for patient management as well as pathogen characterization e.g.,MRSA, ESBL, CPE, VRE etc. CHROMagar may enable a collection of pathogensto be presumptively identified i.e., CHROMagar orientation for both GramNegative (GN) and Gram Positive (GP) bacteria. Pathogen specific mediamay be used for specimens—such as SS media for Salmonella, Shigella fromstool. Certain organisms may be presumptively identified or flagged onmore generic media based on, for example, Hemolytic properties on bloodagar, or unique morphologic properties on specific media. A potentialcollection of modules with pathogen x media capabilities is summarizedin the Table of FIG. 4 .

3. Quadrant Quantitation App/Module 1030

Based on a streaking pattern, e.g., a BD Kiestra™ InoqulA™ quadrantstreaking pattern, this Quadrant Quantitation module 1030 (See, FIG. 2), in case of detected growth, will provide a growth level being light,moderate or heavy. BD Kiestra™ InoqulA™ automates the processing of bothliquid and non-liquid bacteriology specimens to help streamlineworkflow, enable standardized processes and ensure consistent andhigh-quality streaking for inoculation of solid growth media. The growthlevel will be returned as a vector of three probabilities (light,moderate or heavy) ranging on [0,1] and summing up to 1. For example,the Module may evaluate all plates to assess whether there is no growthor different amounts of growth (e.g., +, ++, +++) and whether any growthis pure, predominate or complex. A no Growth determination can resultoptionally in auto release, or batch release. In some cases, growthquantification (e.g., +, ++, +++) may be determined by three or morecolonies in any particular quadrant.

With respect to growth type, the images/plates may be characterized aspure, predominant and complex. This may be based on a minimal number ofisolated colonies of each type. For example, predominant growth may begreater than (or equal two) two colony types, where one type is greaterthan a factor (e.g., 10 times) the other(s). Complex may be greater thantwo colony types with no predominate isolate or if the isolate is notidentifiable within a presumptive ID table such as the example of FIG. 3. Complex plates may be automatically flagged/passed to manualinterpretation. Pure and predominant plate types could be automaticallypassed for further automated processing such as for havingrepresentatives of each colony type indicated, with rules that drivefurther workup (e.g., picking, ID and/or AST).

For example, a colony forming unit (CFU)/mL Quantitation App/Module 1040(See, FIG. 2 ) may be implemented. Based on InoqulA™ streaking pattern#4 (MonoPlates) or #6 (BiPlates), this module may provide a growth levelbeing in <1, 1 to 9, 10 to 99, 100 to 999, ≥1000 CFU/media on plate. Thegrowth level may be returned as a vector of 5 probabilities ranging on[0,1] and summing up to 1. To get equivalent CFU/ml buckets units, thedispense volume may need to be considered.

In general, the Quantification module may, in some versions, determineif growth is due to a single growing organism, a predominant organism ora mixture of (multiple) organisms. A pure organism may be deemed to bean organism responsible for ≥99% of the observable/imageable growth. Apredominant organism may be an organism responsible for (90%, 99%) ofthe observable/imageable growth. A purity level may be returned as avector of probabilities (e.g., 3 probabilities as pure, predominant,complex) ranging on [0,1] and summing up to 1. In the case of pure orpredominant growth up to five colony locations for the main organismwill be given in decreasing probabilities.

Examples of responses to determined quantification are as follows:

1) An image of a specimen on a plated media is determined to havegreater than a predetermined threshold (100,000 CFU/mL) of mixed flora.The response of the App to this determination is to flag this plate as acomplex plate, because the plate has over the threshold amount of mixedflora and recommend manual review of the plate. Such a determination isnot made in the context of media or taxa so this is an App of broadapplicability and not limited in deployment to specific media or taxa onthe plate.2) An image is evaluated and determined to exhibit no growth for 24 hrs.If the specimen is classified as a critical specimen the App issues apreliminary report of no growth and either recommends or controls there-incubation of the plate for another 24 hours. If a subsequent imagedetects no growth in 48 hours, the App sends a final report to the userof no growth after 48 hours. The App either recommends or controlsdiscarding the plate3) An image of a specimen on a plated media is determined to havegreater than a predetermined threshold (100,000 CFU/mL) of a pure growthand a colony size that exceeds 0.5 mm. The response of the App is toissue an instruction or control the pick of colony for ID and ASTtesting. The App will flag the specimen for review by the technician andwill send the greater than 100,000 CFU/mL report to the physicianassociated with the specimen.4) An image of a specimen is determined to reveal the presence of MRSAby the App. The App sends a report that MRSA is detected and adds thespecimen to a positive MRSA work list. If the App determines that thesize of the MRSA colony exceeds a threshold (e.g. greater than 0.5 mm)the App will issue an instruction or control sending the sample for IDand AST testing. As noted elsewhere herein, ID and AST have their ownspecimen work up and evaluation. As such, ID and AST systems andapparatus are typically downstream of the incubation/imaging apparatus(e.g. Kiestra™ ReadA compact).5) An image of a specimen classified as ESBL is determined to show nogrowth. The App will issue a final report that no ESBL isolate wasdetected and will issue an instruction to discard or control discard ofthe plate.6) An image of specimen classified as sputum is identified as havingmixed flora (therefore a complex plate) that exceeds the thresholdamount. When the App determines that the plate is complex it issues aninstruction for a technician to review the plate. Note that differentthresholds for mixed flora that trigger the requirement for manualreview might be deployed depending on specimen classification.7) An image of a specimen classified as critical on blood agar isdetermined to reveal growth. In such an instance, the critical specimenApp would fire and send an alert to the physician associated with thespecimen and cause the specimen to be added to the critical sample worklist. If the App determines that a colony greater than a threshold size(i.e. greater than 0.5 mm) and specimen is classified as being disposedon MacConkey agar, then the App causes the specimen to be sent to autopick for MALDI and for Gram negative AST. The App will also send areport indicating that a Gram-negative specimen has been isolated.8) An image of a specimen reveals a colony number that is greater than100,000 CFU/mL and classifies the image as pure and having a colony sizegreater than a predetermined threshold (e.g. greater than 0.5 mm). Inresponse, the App causes the specimen to be auto picked for AST, causesthe specimen to be added to the positive review list by a technician andcauses a report to be sent to the physician associated with the samplethat indicates that more than 100,000 CFU/mL of a colony was detectedfrom the sample. Further, if the AST results reveal that the pickedcolony is resistant to Carbapenem, then the App causes a molecularconfirmatory test to be performed.9) An image of a specimen is determined to possess heavy predominantgrowth. In response, the App causes the growth to be auto picked and asuspension prepared for performing MALDI on the picked sample. If theMALDI identifies the colony as E. Coli, then the App causes the sampleto be further evaluated for Gram Negative AST (using either the MALDIsuspension or a new pick of the colony).

In some versions, growth detection may be implemented as two moduleswhere one evaluates plates to determine growth/no growth and anothermodule evaluates growth quantity (+, ++, +++) in 3 or more colonies inany particular quadrant. For example, a first module evaluates an imageof a critical, normally sterile specimen. If that evaluation revealsgrowth at a predetermined time point, and determines that a colony sizeis greater that a predetermined threshold size, then the App identifiescoordinates of representative colonies and issues instructions to theimaging apparatus (e.g. ReadA) that the plate is to be moved to anapparatus that will auto pick the identified colony. The picked colonyis resuspended in a solution to a predetermined density for furthertesting in, e.g., a molecular diagnostic apparatus or test (e.g., PCR,Sequencing).

4. Presumptive ID App/Module 1050

In case of pure or predominate growth, a Presumptive ID module 1050(See, FIG. 2 ) may identify the main organisms potentially growing on agiven media using a set of identification algorithms based on thetraining with the Data Lake. This module may provide/output a name of ahighest ranking (e.g., probability) organism (or organism groups) and upto five colony locations ranked from highest to lowest probability forthat identification. These algorithms enable identification of specificspecies on specific media types where any number of colonies are presentand considered clinically significant.

For example, the media and colony identities may be those indicated inthe Table of FIG. 3 . Rules that action workup of these colonies can beincluded in the Module. As an example of a specific IDApp, a urineculture App (UCA) and Chrom ID App could enable presumptive ID onOrientation CHROMagar from urines for those organisms claimed by themedia. Rules would apply the ability to auto report/auto release (orbatch), and downstream workup (e.g., automatic picking, testing, etc.).In some cases, rules may determine High Value Positives to flag platesthat should be rapidly reviewed and directed for further process by awork list or automated pick, etc.

4.1 Purity Plate App/Module

In some versions, the system may implement a purity plate module. Pure,predominant and complex plates may require a minimal number of isolatedcolonies in each. Predominant growth has typically greater than twocolony types, where one colony type is greater than ten times the othercolony type. Complex type typically has greater than two colony typeswith no predominate isolate or if the isolate is not identifiable(presumptive ID table below). Complex plates are typically manuallyinterpreted. Thus, the module may classify the plate image according towhether it is pure, predominant (which can be slightly mixed) and/orcomplex.

4.2 AutoSelect ID/AST Module

Pure and predominant plates can have representatives of each colony typeindicated by the Purity Plate Mod with associated Rules that drivefurther workup as set forth in the examples above. A pre-determinednumber of each colony type could be designated for automaticidentification (ID) and AST workup on an ID/AST module that may involvean automatic picking system/robot.

5. Kirby-Bauer (KB) Zone Diameter Measurement App Module

Some embodiments may utilize an optional measurement App. Such an Appmay leverage existing imaging capabilities and the AST Expert Systems toprovide zone measurements. Optionally these measurements may be linkedto expert systems to provide interpretations. Opportunities also existfor a version of this App for Early zone measurements for particulardrug/organism combinations and for zones on media plated directly frompositive blood culture. Such algorithms may be based on metadata and/orimages of the Data Lake.

In some versions, this App would leverage existing imaging capabilitiesand an AST Expert System to provide zone measurements and Abx Diskidentification. Optionally these measurements could be linked to expertsystems to provide guidance on an antibiotic susceptibility profile fora pathogen isolated from the patient and guidance on treatment/response.In some versions, implementations of this App may provide early zonemeasurements for particular drug/organism combinations and for zones onmedia plated directly from positive blood culture. Some Apps may includeexpert systems (interpretations) and could be facilitated significantlywith the Data Lake being stocked, monitored, audited appropriately andwith required metadata and images.

Although a system 100 may include any one or more of the above imagingrelated modules/Apps, in some versions a particular segmentation offunctionality of the modules may be implemented by the following set ofdiscrete modules/Apps: (a) Quadrant-Quantitation; (b) Detection of NoGrowth; (c) Purity Plate: #Colony Types (e.g., pure, Predominant,Complex) (d) Screening (e.g., CHROMagar i.e. MRSA); (e) Criticalpathogens; (f) Early Growth Detection; (g) Auto Select ID/AST and (h)Zone measurement Kirby-Bauer.

Imaging Apps and Launch Packages

The system 100, providing the combination of algorithms/modules/rules,the Data Lake, and the ability to extract predetermined subsets of data,can enable an on-demand ability to rapidly mature algorithms and developApps. As an example, Apps may be developed based on specimen type andmay be implemented with a collection of Apps. The strategy to implementa collection of Apps is influenced by many factors: supporting softwarelaunch cadence; value of individual Apps vs Apps being together; theavailability of certain algorithms or specimen/plate/organism types inthe Data Lake, etc.

An example may be considered in relation to the following table:

TABLE 1 Exemplary Modules and Their Functions Module Number Module NameFunction 1 Urine Culture Quantitation into 5 buckets; apply Quantitationuser threshold rules 2 Urine Culture Auto Release (in Europe) of NoAuto-Negative Release Significant Growth Plates 3 Urine CultureQuantitation into 5 buckets on each Quantitation-BiPlate half with rules4 Urine Culture Early Earliest Growth Detected on Plate Growth DetectionProvides a Notification to a User 2.1 Urine Culture Batch Release (inthe US) of No Batch-Negative Release Growth Plates 5 Urine PresumptiveID Orientation CHROMagar-based ID of claimed taxa

In this specimen-based example, a series of 5 different Modules concernsone specimen type. Apps validated against a particular specimen type isone way to package functionality, however, an Exponential approach willalso allow other options. For example, Surveillance Apps will allowlaunch by particular targeted organism (MRSA, Streptococcus, Shigella);Quantitation Apps could be packaged by media type (quantity of thesample on blood agar, independent of specimen), etc. In this sense,however, certain Apps will likely have minimal value for certainSpecimens (i.e., quantity of the sample on nonselective media withsputum, given high normal flora levels). Note that the Apps can bedelimited by region with specific rules and specific functions limitedto the geographic region from which the specimen under evaluation wasobtained. Table 1 identifies functions specific to clinical requirementsparticular to the United States (US) and Europe (EU).

Thus, potential Apps may be segregated into two-high-level buckets. Afirst bucket collection may be considered screening and keyidentification Apps. Such Apps typically target specific organisms onCHROMagars, or for high-value pathogens on, for example, Blood agar.Each of these are discrete and can be prioritized for development withminimal impact to other Apps or specimen types and launched individuallyif desired. Similarly, the Kirby-Bauer zone App is generally independentof the next generation Apps (“Next Gen App”), which have associatedalgorithms that can be independently prioritized. Additionally, as newCHROMagars (i.e., vancomycin resistant enterococci (VRE)) becomeavailable, appropriate specimens can be run to populate the Data Lakeand be added to this list. If targeted isolates are relative rare, theData Lake may be supplemented with contrived (spiked specimen) samples.Additional example screening and Key ID Apps are illustrated in thefollowing Table.

TABLE 2 App Classification, Construction, Function and Output DataRequired App Type of Output/Instruction to Train and App CategoryClassification App Function Specimen From App Run App Critical Group BStrep Auto Negative Report urine Auto-Neg (EU only) Training Data;Pathogen on CHROMagar for Group B Strep Batch-US and EU data lakeOrientation initial batch Auto Neg-US FDA validation; review and releaseclinical auto negative release submission in the US with clinicalsubmission Critical MRSA on Flag growth for Nasopharyngeal Auto-Neg (EUonly) Training Data; Pathogen CHROMagar MRSA perirectal Batch-US and EUdata lake MRSA II initial batch Auto Neg-US FDA validation; review andrelease; clinical auto negative release submission in the US withclinical submission Critical Group B Strep Flag growth for VaginalAuto-Neg (EU only) Training Data; Pathogen on TSA Blood Group B StrepBatch-US and EU data lake Agar initial batch Auto Neg-US FDA validation;review and release clinical auto negative release submission in US withclinical submission Critical Group A Strep Flag growth forNasopharyngeal Auto-Neg (EU only) Training Data; Pathogen on SelectiveGroup A Strep perirectal Batch-US and EU data lake Strep Agar andinitial batch Auto Neg-US FDA validation; Blood Agar review and releaseclinical auto negative release submission in US with clinical submissionCritical CHROMagar Flag growth for Nasopharyngeal Auto-Neg (EU only)Training Data; Pathogen Carbapenems CPE perirectal Batch-US and EU datalake initial batch Auto Neg-US FDA validation; review and releaseclinical auto negative release submission in US with clinical submissionCritical CHROMagar Flag growth Nasopharyngeal Auto-Neg (EU only)Training Data; Pathogen ESBL for ESBL perirectal Batch-US and EU datalake initial batch Auto Neg-US FDA validation; review and releaseclinical auto negative release submission in US with clinical submissionCritical Salmonella and Flag growth for Stool Auto-Neg (EU only)Training Data; Pathogen Shigella on Salmonella and Batch-US and EU datalake Hecktoen, XLD Shigella Auto Neg-US FDA validation; and SS agarinitial batch clinical review and release submission auto negativerelease in US with clinical submission Critical N. gonorrhea Flag growthfor urogenital Auto-Neg (EU only) Training Data; Pathogen on Thayer N.gonorrhea Batch-US and EU data lake Martin media initial batch AutoNeg - US FDA validation; review and release clinical auto negativerelease submission in US with clinical submission Critical HaemophilusFlag growth for Respiratory Auto-Neg (EU only) Training Data; Pathogenon Chocolate Haemophilus Batch-US and EU data lake Agar initial batchAuto Neg-US FDA validation; review and release clinical auto negativerelease submission in US with clinical submission KB zone Zone Zonemeasurement, Colony to KB EU Training Data; measurement user review andUS data lake linked to Abx release validation. code KB zone SIR ZoneZone measurement, Colony to KB EU Training Data; measurement user reviewand US data lake linked to Abx release validation. code and simple lookup table for SIR KB zone Zone + Abx code Zone measurement linked Colonyto KB EU Training Data; expert linked to an Expert to EuCAST ExpertSystem US data lake validation, System defined may require new rules fororganism × drug (EU); organism × drug certain organism × drug, For USadd clinical Abx ID; software submission Zone measurement linked toFDA/CLSI Expert System KB early zone Zone + Abx Zone measurement, Colonyto KB EU Training data, digital code at 10-12 user review and US lakevalidation, hours release organism × drug (EU); for US add manual v.digital submission. KB early zone Zone Zone measurement linked Colony toKB EU Training data, digital expert measurement; to EuCAST Expert SystemUS lake validation, Expert System defined may require new rules fororganism × drug (EU); organism × drug certain organism × drug, for USadd clinical Abx ID; submission software. Zone measurement linked toFDA/CLSI Expert System KB zone-positive Zone measurement; Zonemeasurement linked Colony to KB EU Training data, database blood cultureExpert System to novel expert system US validation, expert definedorganism × rules (EU) for US add drug FDA submission and organism × drug

It can be observed from Table 2 that Apps can be quite specific and thatthe outputs can depend on specimen classification (i.e. type ofspecimen, US region or EU region, etc.). Table 2 also illustrates at ahigh level, the type of data used to train the App.

A second bucket collection of Apps use more generic algorithms and canbe prioritized and grouped for launch by several criteria. A summary ofexamples of these Apps is provided in Table 3 below. In essence, eachcell in Table 3 represents an App. Cells in the Table below that sharethe same number are appropriate capabilities for that specimen typewhere the shared cell numbers are reasonably packaged together in acommon module. With this model, there are 8 additional launchpackages/modules.

TABLE 3 Sterile Fluids and Superficial Gastro- Capability Urine TissueWounds Respiratory Throat Intestinal Urogenital No Growth 1 1 1 2 2 2 2Quadrant 2 3 6 7 7 8 9 Growth Score (+, ++, +++) Plate purity 5 3 6 7 78 9 (pure, predominant, complex Presumptive 5 3 6 7 7 8 9 ID on pure,predominant plates Early 5 4 4 2 2 2 2 Growth Detection Auto picking 5 36 7 7 8 9 pure, predominantExample Imaging Modules May be Considered with Reference to theFollowing Table:

TABLE 4 List of Exemplary Modules Imaging Module Mod 1. UCA 1.0 UrineCulture Semi-Quantitation Mod 2. UCA 1.0 Urine Culture Auto-NegativeRelease Eu Mod 3. UCA 1.5 Urine Culture Semi-Quantitation-BiPlates Mod4. UCA 1.5 Urine Culture Early Growth Detect Mod 2.1 UCA 1.5 UrineCulture batch-Negative Release (US) Mod 5. UCA 1.8 Presumptive ID BBLOrientation CHROMagar for 6 groups; Group B Strep flag Mod 6. UCA 1.8Pure/Predominant/Complex on BBL Orientation CHROMagar Mod 6.2 UCA 2.0:UCA 1.8-autorelease US. 510K approval. 25 mp camera launch Mod 7.Surveillance: MRSA on BBL CHROMagar. Batch Release EU/US Mod 8.Surveillance: Carbepenem producing Enterobacteriaciae (CPE) on BBLCHROMagar Mod 9. Surveillance: ESBL on BBL CHROMagar Mod 9. CriticalSpecimens: Early Growth (sterile fluids, superficial wound, tissue,urine) Mod 10. Critical Specimens: Presumptive ID Mod 11. CriticalSpecimens: Quadrant quantitation, Mod 12 Critical Specimens:Pure/Predominant/Complex Mod 13. Critical Specimens: No Growth Detection(user defined time pts). Batch/Auto Mod 14. Critical Specimens: AutoSelect ID/AST (Wagtail) Mod 15. KB Zone (disk ID, Zone size) manualinterpretation Mod 16 KB Zone SIR (susceptible, intermediate, resistant)manual release Mod # Critical Pathogen: Group A Strep on Selective StrepAgar, and Blood Agar Mod # Respiratory: Quadrant quantitation; Mod #Respiratory: Pure/predominant/complex Mod # Respiratory: Presumptive IDMod # Respiratory: Auto Select ID/AST Mod # Respiratory: CriticalPathogen: Haemophilus on chocolate agar Mod # Stool: Negative growthbatch report from enrichment culture on selective agar Mod # Stool:Negative growth batch reporting from primary culture Mod # Stool:Quadrant quantitation Mod # Stool: Pure/Predominant/Complex Mod # Stool:Critical Pathogen: Salmonella and Shigella on Hecktoen, XLD, and SSmedia Mod # Stool: Auto Pick (Wagtail) Mod # KB-Zone-expert System. CLSI(Clinical and Laboratory Standards Institute)/EUCAST guidelines. SIR

EUCAST is the European Committee on Antimicrobial SusceptibilityTesting. In one example of a process integrated with one or more Appsfor specimen evaluation and process control, a specimen is inoculatedonto a plated media using BD Kiestra™ InoqulA. The specimen is streakedonto the media using a predetermined pattern which is tracked as part ofmetadata via the bar code. The streaked sample is conveyed into BDKiestra™ ReadA compact where it is incubated and imaged at timesdetermined by the App. The images obtained by ReadA at the appointedtime is analyzed by the App to determine if the specimen on the plate ispure. Further work up of the specification is performed based on theresults of the determination. The image is evaluated to identify thecoordinates of the selected colonies. The App can convey thosecoordinates to an apparatus (or technologist). The App can issueinstructions to convey the specimen to the apparatus in which the colonywill be picked. The App can further coordinate or control the picking ofthe colonies and conveying the picked colonies to another platform whereID of the pathogen is performed. In one example ID is performed byMALDI. As described elsewhere herein, a sample is evaluated by MALDI byplacing the picked sample into suspension and inoculating a MALDI platewith the suspension. The App can also coordinate or control transfer ofthe colony suspension to BD Kiestra™ InoqulA. There the suspension canbe inoculated onto another type of culture media (e.g., Mueller Hinton)using a “spread pattern” and then moved to an AST testing apparatuswhere predetermined antibiotic disks (e.g. BD BBL™ Sensi-Discs™) areplace on the culture. The plate carrying the inoculated specimen and theantibiotic disks is then conveyed to a ReadA compact under coordinationand control of the App. The ReadA obtains images and provides thoseimages to the App, the results of which are conveyed from the App to anExpert System for analysis of the resulting antibiotic disk zones andinterpretation of the results. The Expert System then conveys theresults of the analysis to the clinical lab staff

Although the invention herein has been described with reference toparticular embodiments, it is to be understood that these embodimentsare merely illustrative of the principles and applications of thepresent invention. It is therefore to be understood that numerousmodifications may be made to the illustrative embodiments and that otherarrangements may be devised without departing from the spirit and scopeof the present invention as defined by the appended claims.

What is claimed is:
 1. A system for detection of microbial growth,colony counting and/or identification comprising: a database systemcomprising: (a) digital images of microbial specimens and historicalimage data; (b) determined values indicative of quantity for themicrobial specimens of the digital images; and (c) identifications ofspecies of organisms for the microbial specimens of the digital images;one or more processor readable mediums with processor controlinstructions, the processor control instructions defining a discrete setof application modules, the discrete set of application modulescomprising: a growth detector configured to process a digital image of agrowth medium based on a set of pre-set imaging locations from thedigital image and further based on a comparison of the digital imagewith the historical image data and generate a growth indicatorcomprising a probability value that represents a probability ofmicrobial growth occurring in the growth medium; a growth quantitatorconfigured to process the digital image of the growth medium from thegrowth detector, the growth quantitator configured to generate a growthlevel quantification from the digital image based on a comparison of thedigital image with the historical image data as one or more of aprobability of light growth, a probability of moderate growth, and aprobability of heavy growth; and a presumptive identifier configured toprocess the digital image from the growth quantitator, the presumptiveidentifier configured to generate name indicators of a set of microbialspecimens of the digital image based on training with digital images ofthe database system.
 2. The system of claim 1, wherein the growth levelquantification comprises a set of probabilities ranging from 0 to
 1. 3.The system of claim 2, wherein a sum of the set of probabilities is 1.4. The system of claim 1, wherein the database system is coupled to anetwork to receive data from one or more clinical laboratories includingimaging systems for generating digital images of microbial specimens ongrowth mediums.
 5. The system of claim 1, wherein the database systemfurther comprises time of image capture data and image capture conditiondata concerning the digital images of microbial specimens.
 6. The systemof claim 5, wherein the database system further comprises media typedata.
 7. The system of claim 1, wherein the discrete set of applicationmodules further comprises a zone measurer, the zone measurer configuredto generate one or more measurements of a zone of growth.
 8. A systemfor detection of microbial growth, colony counting and/or identificationcomprising: a database system comprising: (a) digital images ofmicrobial specimens, including historical image data (b) determinedvalues indicative of quantity for the microbial specimens of the digitalimages; and (c) identifications of species of organisms for themicrobial specimens of the digital images; one or more processorreadable mediums with processor control instructions, the processorcontrol instructions defining a discrete set of application modules, thediscrete set of application modules comprising any three or more of: agrowth detector configured to process a digital image of a growth mediumbased on a set of pre-set imaging locations from the digital image andfurther based on a comparison of the digital image with the historicalimage data and generate a growth indicator comprising a probabilityvalue that represents a probability of microbial growth occurring in thegrowth medium; a growth quantitator configured to process the digitalimage of the growth medium from the growth detector, the growthquantitator configured to generate a growth level quantification fromthe digital image based on a comparison of the digital image with thehistorical image data; a purity detector, the purity detector configuredgenerate a categorization of the digital image of a growth mediumaccording to a set of purity levels; a key organism identifier, the keyorganism identifier configured to generate, based on training withdigital images of the database system, a set of probabilities indicatinglikelihood that the digital image of a growth medium contains coloniesof a set of input species requests; a volume quantitator, the volumequantitator configured to generate a probability indicating likelihoodthat the digital image of a growth medium contains a growth volumequantification for a set of volume ranges; and a presumptive identifierconfigured to process the digital image from the growth quantitator, thepresumptive identifier configured to generate name indicators of a setof microbial specimens of the digital image based on training withdigital images of the database system.
 9. A system for detection ofmicrobial growth, colony counting and/or identification comprising: adatabase system comprising: (a) digital images of microbial specimensand historical image data; (b) determined values indicative of quantityfor the microbial specimens of the digital images; and (c)identifications of species of organisms determined to be of clinicalsignificance for the microbial specimens of the digital images; one ormore processor readable mediums with processor control instructions, theprocessor control instructions defining a discrete set of applicationmodules, the discrete set of application modules comprising: a growthdetector configured to process a digital image of a growth medium basedon a set of pre-set imaging locations from the digital image and furtherbased on a comparison of the digital image with the historical imagedata and generate a growth indicator comprising a probability value thatrepresents a probability of microbial growth occurring in the growthmedium; a growth quantitator configured to process the digital image ofthe growth medium from the growth detector, the growth quantitatorconfigured to generate a growth level quantification from the digitalimage based on a comparison of the digital image with the historicalimage data; and a presumptive identifier configured to process thedigital image from the growth quantitator, the presumptive identifierconfigured to generate name indicators of a set of microbial specimensof the digital image based on training with digital images of thedatabase system, wherein the presumptive identifier is configured togenerate the name indicators by generating probabilities for each of thename indicators as a list ranking the name indicators by the generatedprobabilities.
 10. The system of claim 9, wherein the presumptiveidentifier is configured to provide the list ranking for each of aplurality of detected colony locations in the growth medium of thedigital image.
 11. A system for detection of microbial growth, colonycounting and/or identification comprising: a database system comprising:(a) digital images of microbial specimens and historical image data; (b)determined values indicative of quantity for the microbial specimens ofthe digital images; and (c) identifications of species of organismsdetermined to be of clinical significance for the microbial specimens ofthe digital images; one or more processor readable mediums withprocessor control instructions, the processor control instructionsdefining a discrete set of application modules, the discrete set ofapplication modules comprising: a growth detector configured to processa digital image of a growth medium based on a set of pre-set imaginglocations from the digital image and further based on a comparison ofthe digital image with the historical image data and generate a growthindicator comprising a probability value that represents a probabilityof microbial growth occurring in the growth medium; a growth quantitatorconfigured to process the digital image of the growth medium from thegrowth detector, the growth quantitator configured to generate a growthlevel quantification from the digital image based on a comparison of thedigital image with the historical image data; a purity detector, thepurity detector configured generate a categorization of the digitalimage of a growth medium according to at least one predetermined puritylevel; and a presumptive identifier configured to process the digitalimage from the growth quantitator, the presumptive identifier configuredto generate name indicators of a set of microbial specimens of thedigital image based on training with digital images of the databasesystem.
 12. The system of claim 11, wherein the discrete set of puritylevels comprises a pure level, a predominate level and a mixed floralevel.
 13. The system of claim 11, wherein the discrete set of puritylevels comprises probabilities for each level.
 14. The system of claim13, wherein each probability of the discrete set of purity levelprobabilities range from 0 to
 1. 15. The system of claim 14, wherein asum of the probabilities for the set of purity levels equals
 1. 16. Thesystem of claim 11, wherein the purity detector generates a pure levelcharacterization when a single organism is responsible for detectablegrowth.
 17. The system of claim 11, wherein the purity detectorgenerates a predominant level characterization when a single organism isresponsible for detectable growth in a predetermined percentage range.18. The system of claim 17, wherein the predetermined percentage rangeis 90 to 99 percent of detected growth.
 19. The system of claim 17,wherein the purity detector generates a mixed flora levelcharacterization when a single organism is responsible for detectablegrowth below the predetermined percentage range.
 20. A system fordetection of microbial growth, colony counting and/or identificationcomprising: a database system comprising: (a) digital images ofmicrobial specimens and historical image data; (b) determined valuesindicative of quantity for the microbial specimens of the digitalimages; and (c) identifications of species of organisms determined to beof clinical significance for the microbial specimens of the digitalimages; one or more processor readable mediums with processor controlinstructions, the processor control instructions defining a discrete setof application modules, the discrete set of application modulescomprising: a growth detector configured to process a digital image of agrowth medium based on a set of pre-set imaging locations from thedigital image and further based on a comparison of the digital imagewith the historical image data and generate a growth indicatorcomprising a probability value that represents a probability ofmicrobial growth occurring in the growth medium; a growth quantitatorconfigured to process the digital image of the growth medium from thegrowth detector, the growth quantitator configured to generate a growthlevel quantification from the digital image based on a comparison of thedigital image with the historical image data; a key organism identifier,the key organism identifier configured to generate, based on trainingwith digital images of the database system, a probability indicatinglikelihood that the digital image of a growth medium contains a colonyof an input species request; and a presumptive identifier configured toprocess the digital image from the growth quantitator, the presumptiveidentifier configured to generate name indicators of a set of microbialspecimens of the digital image based on training with digital images ofthe database system.
 21. The system of claim 20, wherein the keyorganism identifier is configured to access a set of rules trained withdigital images of the database system, the set of rules configured forclassifying the digital image of a growth medium inoculated withspecimen with respect to the input species request.
 22. A system fordetection of microbial growth, colony counting and/or identificationcomprising: a database system comprising: (a) digital images ofmicrobial specimens and historical image data; (b) determined valuesindicative of quantity for the microbial specimens of the digitalimages; and (c) identifications of species of organisms determined to beof clinical significance for the microbial specimens of the digitalimages; one or more processor readable mediums with processor controlinstructions, the processor control instructions defining a discrete setof application modules, the discrete set of application modulescomprising: a growth detector configured to process a digital image of agrowth medium based on a set of pre-set imaging locations from thedigital image and further based on a comparison of the digital imagewith the historical image data and generate a growth indicatorcomprising a probability value that represents a probability ofmicrobial growth occurring in the growth medium; a growth quantitatorconfigured to process the digital image of the growth medium from thegrowth detector, the growth quantitator configured to generate a growthlevel quantification from the digital image based on a comparison of thedigital image with the historical image data; a key organism identifier,the key organism identifier configured to generate, based on trainingwith digital images of the database system, a set of probabilitiesindicating likelihood that the digital image of a growth medium containscolonies of a set of input species requests; and a presumptiveidentifier configured to process the digital image from the growthquantitator, the presumptive identifier configured to generate nameindicators of a set of microbial specimens of the digital image based ontraining with digital images of the database system.
 23. The system ofclaim 22, wherein the key organism identifier is configured to access aplurality of sets of rules trained with digital images of the databasesystem, wherein each set of rules of the plurality of sets of rules isconfigured for classifying the digital image of a growth medium withrespect to one species of the set of input species requests.
 24. Asystem for detection of microbial growth, colony counting and/oridentification comprising: a database system comprising: (a) digitalimages of microbial specimens and historical image data; (b) determinedvalues indicative of quantity for the microbial specimens of the digitalimages; and (c) identifications of species of organisms determined to beof clinical significance for the microbial specimens of the digitalimages; one or more processor readable mediums with processor controlinstructions, the processor control instructions defining a discrete setof application modules, the discrete set of application modulescomprising: a growth detector configured to process a digital image of agrowth medium based on a set of pre-set imaging locations from thedigital image and further based on a comparison of the digital imagewith the historical image data and generate a growth indicatorcomprising a probability value that represents a probability ofmicrobial growth occurring in the growth medium; a growth quantitatorconfigured to process the digital image of the growth medium from thegrowth detector, the growth quantitator configured to generate a growthlevel quantification from the digital image based on a comparison of thedigital image with the historical image data; a volume quantitator, thevolume quantitator configured to generate a probability indicatinglikelihood that the digital image of a growth medium contains a growthvolume quantification for a set of volume ranges; and a presumptiveidentifier configured to process the digital image from the growthquantitator, the presumptive identifier configured to generate nameindicators of a set of microbial specimens of the digital image based ontraining with digital images of the database system.
 25. The system ofclaim 24, wherein the volume quantitator generates a probability foreach range of the set of volume ranges.
 26. The system of claim 25,wherein the volume quantitator generates the probability for each rangeof the set of volume ranges as a probability value from 0 to
 1. 27. Thesystem of claim 26, wherein a sum of the probability values is
 1. 28. Asystem for detection of microbial growth, colony counting and/oridentification comprising: a database system comprising: (a) digitalimages of microbial specimens and historical image data; (b) determinedvalues indicative of quantity for the microbial specimens of the digitalimages; and (c) identifications of species of organisms determined to beof clinical significance for the microbial specimens of the digitalimages; (d) deidentified patient demographic data; one or more processorreadable mediums with processor control instructions, the processorcontrol instructions defining a discrete set of application modules, thediscrete set of application modules comprising: a growth detectorconfigured to process a digital image of a growth medium based on a setof pre-set imaging locations from the digital image and further based ona comparison of the digital image with the historical image data andgenerate a growth indicator comprising a probability value thatrepresents a probability of microbial growth occurring in the growthmedium; a growth quantitator configured to process the digital image ofthe growth medium from the growth detector, the growth quantitatorconfigured to generate a growth level quantification from the digitalimage based on a comparison of the digital image with the historicalimage data; and a presumptive identifier configured to process thedigital image from the growth quantitator, the presumptive identifierconfigured to generate name indicators of a set of microbial specimensof the digital image based on training with digital images of thedatabase system.